Dynamic traffic signalling based on the density of vehicle traffic in urban areas using data analysis techniques

  • Authors

    • P VinaySai Kumar
    • Dr Mohammed Ali Hussain
    2018-03-18
    https://doi.org/10.14419/ijet.v7i2.7.10752
  • Big Data, Traffic Lights, Smart Traffic Management, Machine Learning, Hadoop.

  • Abstract

    With the advent of the urban population,the need for effectivetraffic management has taken its prominence. There are many studies with various typesof solutions. Our study focuses on how the time can be reduced based on the traffic density in that area and the previous area. The present scenario is an acutesystem of traffic lights, so our proposal is based on the analysis of previous junction timer and the traffic lights will adaptdepending on traffic conditions. The machine learning techniques like reinforcement learning are being used to reduce commute time and will save the humans fromlethal pollution. This will eventually increase the life span of the humans.

     

     

  • References

    1. [1] https://esa.un.org/unpd/wup/publications/files/wup2014-highlights.pdf.

      [2] https://www.linkedin.com/pulse/how-control-traffic-roads-indian-cities-any-other-part-marathe.

      [3] https://www.linkedin.com/pulse/iot-equation-most simplified-article-modern-ecosystem-shady.

      [4] https://www.researchgate.net/publication/310036684_IoT Based_Traffic_Management_System.

      [5] Chunxiao Li and Shigeru Shimamoto,†Etc Assisted Traffic Light Control Scheme For Reducing Vehicles’ Co2 Emissionsâ€. International Journal of Managing Information Technology (IJMIT) Vol.4, No.2, May 2012.

      [6] Laisheng Xiao, “Internet of Things: a New Application for Intelligent Traffic Monitoring Systemâ€. JOURNAL OF NETWORKS, VOL. 6, NO.6, JUNE 2011.

      [7] International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 07 | July-2016â€An Internet of Things Based Real Time Traffic Light Control to Reduce Vehicles CO2 Emissionsâ€.

      [8] O. C. Ubadike, H. M. Jia, and P. C. Brook, "Investigation of Image Fusion Methods for Helicopter Day, Night and All Weather Operation," in Intelligent Vehicles Symposium, Vols 1 and 2, ed New York: IEEE, 2009, pp. 1167-1172.

      [9] G. D. Finlayson, S. D. Hordley, C. Lu, and M. S. Drew, "On the removal of shadows from images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28 (1), pp. 59-68, 2006.

      [10] 2016 International Conference on Emerging Technological Trends [ICETT] Real-Time Smart Traffic Management System for Smart Cities by Using Internet of Things and Big Data.

      [11] Kapileswar Nellore and Gerhard P. Hancke “A Survey on Urban Traffic Management System Using Wireless Sensor Networks†Sensors 2016, 16, 157; doi: 10.3390/s16020157.

      [12] Md. MunirHasan, GobindaSaha, AminulHoque, and Md. BadruddojaMajumder, “Smart Traffic Control System with Application of Image Processing Techniques†3rd International Conference On Informatics, Electronics & Vision 2014.

  • Downloads

  • How to Cite

    VinaySai Kumar, P., & Mohammed Ali Hussain, D. (2018). Dynamic traffic signalling based on the density of vehicle traffic in urban areas using data analysis techniques. International Journal of Engineering & Technology, 7(2.7), 401-403. https://doi.org/10.14419/ijet.v7i2.7.10752

    Received date: 2018-03-28

    Accepted date: 2018-03-28

    Published date: 2018-03-18